R Notebook

names(list_of_cents)

[1] "fold_change_aritmetic" "fold_change_geometric" "fold_change_quadratic" "fold_change_harmonic"  "delta_aritmetic"       "delta_geometric"       "delta_quadratic"       "delta_harmonic"       

Density plot of centralities

plot_density <- function(df, titulo ){cut = 0.01
                             df %>% gather() -> df_gathered
                              df_gathered['value'] %>% filter(abs(value) > cut) %>%
                              ggplot( aes(x=value)) +
                                    geom_density(fill="deepskyblue4", color="azure4", alpha=.8)+
                                       theme(legend.position="top") +
                                    ylab("Density") + ggtitle(titulo) +
                                    xlab("Centrality") + geom_vline(xintercept=0, linetype="dashed", color = "red",  size=.4) -> Density_plot_of_centralities
                              return(Density_plot_of_centralities)}



map2(list_of_cents, names(list_of_cents), plot_density)

$fold_change_aritmetic

$fold_change_geometric

$fold_change_quadratic

$fold_change_harmonic

$delta_aritmetic

$delta_geometric

$delta_quadratic

$delta_harmonic

#centralities <-  Results[,!is.na(as.numeric(colnames(Results)))] %>% gather()

Density_plot_of_centralities.panel <- ggarrange( NULL,Density_plot_of_centralities, nrow = 2,  heights= c(1, .4), labels = c('a','b'),
                                                 hjust = -0.1, vjust = .8)

Density_plot_of_centralities.panel

fba_list_of_cents

$fold_change_aritmetic

$fold_change_geometric

$fold_change_quadratic

$fold_change_harmonic

$delta_aritmetic

$delta_geometric

$delta_quadratic

$delta_harmonic

NA

Hierarchical clustering

library(tidyverse)
library(scales)


assign_node_type <- function(df){
data <- df  %>% rownames_to_column('ID') %>% filter(!str_detect(ID, regex('AA_|AA2', ignore_case = F)))  %>%
         mutate(`Node type` = ifelse(str_detect(Reaction, regex('\\[[a-z]A\\]',              ignore_case = T)), 'Astrocyte', NA)) %>% 
         mutate(`Node type` = ifelse(str_detect(Reaction, regex('\\[[a-z]N\\]',              ignore_case = T)), 'Neuron', `Node type`))    %>% 
         mutate(`Node type` = ifelse( str_detect(Reaction, regex('\\[[a-z]A\\]|\\[[a-z]N\\]',ignore_case = T), negate = T), 'Exchange', `Node type`)) %>%
         mutate(`Node type` = ifelse(str_detect(Reaction, regex('\\[[a-z]A\\]',ignore_case = T)) & 
                                       str_detect(Name, regex('DM_|Demand|sink',ignore_case = T)), 'Sink/Demand Astro', `Node type`))%>%
         mutate(`Node type` = ifelse(str_detect(Reaction, regex('\\[[a-z]N\\]',ignore_case = T)) & 
                                       str_detect(Name, regex('DM_|Demand|sink',ignore_case = T)), 'Sink/Demand Neuron', `Node type`))
                                    return(data)}



map(fba_list_of_cents, assign_node_type) -> data


clean_centralities <- function(df){ df %>% select(-c ("ID", "Name", "Reaction",  "Flux", "Sensitivity","Node type")  )}


map(data, clean_centralities) -> only_centralities

map( data,get_summaryzed.optimality)

$fold_change_aritmetic
                [,1]
   [1,] 7.113276e-01
   [2,] 6.026296e-01
   [3,] 0.000000e+00
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 [ reached getOption("max.print") -- omitted 53 rows ]

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 [ reached getOption("max.print") -- omitted 53 rows ]

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 [ reached getOption("max.print") -- omitted 53 rows ]

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 [ reached getOption("max.print") -- omitted 53 rows ]

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 [ reached getOption("max.print") -- omitted 53 rows ]

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 [ reached getOption("max.print") -- omitted 53 rows ]

$delta_quadratic
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 [ reached getOption("max.print") -- omitted 53 rows ]

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 [863,] 9.670510e-18
 [864,] 0.000000e+00
 [865,] 0.000000e+00
 [866,] 9.327452e-01
 [867,] 3.369869e-01
 [868,] 4.311074e-02
 [869,] 9.670510e-18
 [870,] 2.901153e-17
 [871,] 0.000000e+00
 [872,] 3.369869e-01
 [873,] 1.305519e-16
 [874,] 0.000000e+00
 [875,] 3.094563e-16
 [876,] 0.000000e+00
 [877,] 0.000000e+00
 [878,] 4.311074e-02
 [879,] 0.000000e+00
 [880,] 1.257166e-16
 [881,] 7.736408e-17
 [882,] 0.000000e+00
 [883,] 0.000000e+00
 [884,] 1.534052e-14
 [885,] 4.835255e-18
 [886,] 4.083400e-01
 [887,] 5.257720e-01
 [888,] 2.320922e-16
 [889,] 0.000000e+00
 [890,] 0.000000e+00
 [891,] 0.000000e+00
 [892,] 1.535427e-01
 [893,] 2.841608e-30
 [894,] 2.841608e-30
 [895,] 1.824995e-01
 [896,] 9.283689e-16
 [897,] 4.275692e-16
 [898,] 9.653881e-16
 [899,] 9.670510e-18
 [900,] 6.821318e-01
 [901,] 0.000000e+00
 [902,] 0.000000e+00
 [903,] 0.000000e+00
 [904,] 0.000000e+00
 [905,] 0.000000e+00
 [906,] 0.000000e+00
 [907,] 0.000000e+00
 [908,] 0.000000e+00
 [909,] 0.000000e+00
 [910,] 0.000000e+00
 [911,] 2.551643e-01
 [912,] 3.024742e-01
 [913,] 6.866062e-16
 [914,] 0.000000e+00
 [915,] 0.000000e+00
 [916,] 4.835255e-18
 [917,] 8.142411e-01
 [918,] 0.000000e+00
 [919,] 0.000000e+00
 [920,] 5.948954e-01
 [921,] 5.948954e-01
 [922,] 4.083400e-01
 [923,] 0.000000e+00
 [924,] 1.003920e-29
 [925,] 2.841608e-30
 [926,] 0.000000e+00
 [927,] 2.901153e-17
 [928,] 5.802306e-17
 [929,] 9.283689e-16
 [930,] 3.024742e-01
 [931,] 2.417627e-17
 [932,] 1.535427e-01
 [933,] 4.083400e-01
 [934,] 1.535427e-01
 [935,] 1.824995e-01
 [936,] 3.771499e-16
 [937,] 1.434756e-01
 [938,] 1.434756e-01
 [939,] 2.376135e-01
 [940,] 2.417627e-17
 [941,] 2.376135e-01
 [942,] 2.417627e-17
 [943,] 5.318780e-17
 [944,] 0.000000e+00
 [945,] 0.000000e+00
 [946,] 8.581239e-01
 [947,] 0.000000e+00
 [948,] 1.824995e-01
 [949,] 1.653657e-15
 [950,] 2.562685e-16
 [951,] 4.706308e-16
 [952,] 2.662552e-01
 [953,] 0.000000e+00
 [954,] 1.015404e-16
 [955,] 5.947363e-16
 [956,] 7.736408e-17
 [957,] 0.000000e+00
 [958,] 1.566623e-15
 [959,] 0.000000e+00
 [960,] 6.672652e-16
 [961,] 1.535427e-01
 [962,] 2.898834e-32
 [963,] 0.000000e+00
 [964,] 7.736408e-17
 [965,] 1.535427e-01
 [966,] 1.535427e-01
 [967,] 2.376135e-01
 [968,] 9.670510e-18
 [969,] 4.083400e-01
 [970,] 2.898834e-32
 [971,] 0.000000e+00
 [972,] 0.000000e+00
 [973,] 2.852800e-16
 [974,] 2.417627e-17
 [975,] 0.000000e+00
 [976,] 1.934102e-17
 [977,] 7.736408e-16
 [978,] 2.007721e-15
 [979,] 1.392553e-15
 [980,] 1.450576e-17
 [981,] 2.439427e-15
 [982,] 9.573805e-16
 [983,] 1.208814e-16
 [984,] 4.083400e-01
 [985,] 1.535427e-01
 [986,] 1.535427e-01
 [987,] 0.000000e+00
 [988,] 0.000000e+00
 [989,] 7.736408e-17
 [990,] 0.000000e+00
 [991,] 0.000000e+00
 [992,] 0.000000e+00
 [993,] 4.083400e-01
 [994,] 0.000000e+00
 [995,] 0.000000e+00
 [996,] 0.000000e+00
 [997,] 1.535427e-01
 [998,] 1.434756e-01
 [999,] 1.450576e-17
[1000,] 7.736408e-17
 [ reached getOption("max.print") -- omitted 53 rows ]

heatmap

dim(my.corr.matrices$fold_change_aritmetic)

[1] 1053 1053

my.corr.matrices$delta_aritmetic -> my.corr.mat

left_annotation <- rowAnnotation(`Nodes` = node_types, col = list( `Nodes` = c(
                                    "Astrocyte"        = "deepskyblue3", 
                                    'Exchange'   = 'darkgreen',
                                    'Sink/Demand Astro'='darkgoldenrod',
                                    "Neuron"           = "brown2",
                                    'Sink/Demand Neuron'= 'blueviolet' )))





total.abs.centrality$fold_change_aritmetic  -> a_total.abs.cent
summaryzed.optimality$fold_change_aritmetic -> a_summaryzed.opti

right_annotation <-   
  rowAnnotation(gap = unit(12, "points"),Ce      = anno_barplot(bar_width = 0.01,width = unit(1.5, "cm"), border = T,a_total.abs.cent, gp = gpar(col = 'azure4')),
            Op        = anno_barplot(bar_width = 0.01,width = unit(1.5, "cm") ,border = T, a_summaryzed.opti, gp = gpar(col = 'azure4')))


ht <- Heatmap(my.corr.mat, name = "Correlation",  left_annotation =left_annotation,
                                            right_annotation=right_annotation,
                                 clustering_distance_columns  = function(m)   dist(m, method = 'euclidean'),
                                 cluster_columns              = function(x) fastcluster::hclust(dist(x), "average"),
              
                                clustering_distance_rows   = function(m)   dist(m, method = 'euclidean'),
                                 cluster_rows               = function(x) fastcluster::hclust(dist(x), "average"),
                                row_km = 2,
                                column_km = 2,
                                # row_split = paste0("cluster ", pa$clustering),
                                # column_split = paste0("cluster ", pa$clustering),
                                 border = TRUE,
                                 row_dend_width    = unit(3, "cm"),
                                 row_gap = unit(2, "mm"),
                                 column_gap = unit(2, "mm"),
                                  width = unit(10, "cm"), 
                                height = unit(10, "cm"),
                                column_title = c("Astrocytic cluster", "Neuronal cluster"),
                                column_title_gp = gpar(fontsize = 10),
                                  row_title_rot  = 0,
                                 show_column_names    = F,
                                show_row_names    = F,
                                 row_names_gp = gpar(fontsize = 8),
                                  row_title = c("Astrocytic\n cluster", "Neuronal\n cluster"),
                             
                                 row_title_gp = gpar(fontsize = 10))
my_heatmap = grid.grabExpr(draw(ht))

ht

 upper_panel <- ggarrange( Density_plot_of_centralities.panel,  my_heatmap, ncol = 2, widths = c(.4, 1), heights = c(1,.3), labels = c('','c'),
                                                 hjust = -4, vjust = .8)

upper_panel

Distribution of pairwise correlations

row_order(ht)[[1]] -> astro_cluster
row_order(ht)[[2]] -> neuron_cluster
data$`Node type`   -> Nodes
data$ID[neuron_cluster]     -> neuron_cluster_names
data$ID[astro_cluster]      -> astro_cluster_names
ht@matrix %>% as.data.frame -> heatmap_matrix

heatmap_matrix[neuron_cluster_names,neuron_cluster_names] %>% as.matrix %>% c -> `Neuronal_cluster`
heatmap_matrix[astro_cluster_names,astro_cluster_names] %>% as.matrix %>% c  ->`Astrocytic_cluster` 
heatmap_matrix[astro_cluster_names,neuron_cluster_names] %>% as.matrix %>% c  -> `Neuron_vs_Astrocyte`

data.frame(`Neuronal_cluster`) %>% gather  -> A
data.frame(`Astrocytic_cluster`) %>% gather   -> B
data.frame( `Neuron_vs_Astrocyte`) %>% gather -> C
quads <- rbind(A,B,C)
colnames(quads) <- c("Comparison","Node correlation")

#quads$Comparison %>% unique()

my_comparisons <- list( c("Neuronal_cluster", "Astrocytic_cluster"), 
                        c("Neuronal_cluster", "Neuron_vs_Astrocyte"), 
                        c("Astrocytic_cluster", "Neuron_vs_Astrocyte") )

ggplot(quads, aes(x=Comparison, y=`Node correlation`, fill=Comparison)) + 
    geom_violin(trim=F, colour = "azure4")+  
    stat_compare_means( vjust= -1., hjust= 0, comparisons = my_comparisons, method = "wilcox.test", p.adjust.method = "bonferroni", label = "p.signif")+
   scale_fill_manual(values = alpha(c("deepskyblue3", "blueviolet", "brown2"), .8)) + 
   theme(plot.title = element_text(hjust = 0.5),legend.position="none",  axis.title.x=element_blank(),
         axis.text.y = element_text(angle = 45, hjust = 1), axis.text.x = element_text(angle = 50, hjust = 1))  + 
  #ggtitle("Neuronal self-correlations") + 
  scale_y_continuous(limits=c(-0.7, 2)) + geom_hline(yintercept=0, linetype="dashed", color = "darkred",  size=1)-> corr_comparisons

corr_comparisons

library(ProjectionBasedClustering)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

library(PCAtools)

Loading required package: ggrepel

Attaching package: ‘PCAtools’

The following objects are masked from ‘package:stats’:

    biplot, screeplot

library(magrittr)

Attaching package: ‘magrittr’

The following object is masked from ‘package:purrr’:

    set_names

The following object is masked from ‘package:tidyr’:

    extract

p <- pca(my.corr.mat)

cbind(p$rotated$PC1, p$rotated$PC2) %>% as.matrix %>% as.data.frame-> my_pca




my_pca %>%  cbind( data$`Node type`) %>% set_colnames(c('PC1','PC2','node')) -> to.pca.scatter

summaryzed.optimality -> Op
total.abs.centrality -> Centrality
Centrality -> Ce

b <- ggplot(to.pca.scatter, aes(x = PC1, y = PC2)) 
b +   scale_color_manual(labels = c("Astro", "Exch",'Neu','Si/De Ast', 'Si/De Neu'), values =  c("deepskyblue3",'darkgreen','brown2','darkgoldenrod','blueviolet')) + 
  theme(legend.position="right", legend.box = "vertical")+ geom_point(aes(size = Op, color = node))-> pca_node_RedCosts




b <- ggplot(to.pca.scatter, aes(x = PC1, y = PC2)) 
b +   scale_color_manual(labels = c("Astro", "Exch",'Neu','Si/De Ast', 'Si/De Neu'), values =  c("deepskyblue3",'darkgreen','brown2','darkgoldenrod','blueviolet')) +
  theme(legend.position="right", legend.box = "vertical")+ geom_point(aes( size =  Ce,color = node))-> pca_node_centrality

bottom_panel <- ggarrange( corr_comparisons,pca_node_centrality, pca_node_RedCosts  , ncol = 3, widths  = c(.4,1,1) , labels = c('d','e','f'),
                                                 hjust = 0, vjust = 1)

Removed 3 rows containing non-finite values (stat_ydensity).Removed 3 rows containing non-finite values (stat_signif).Removed 26 rows containing missing values (geom_violin).

ggsave(file="panel_without_graph.png", plot=panel_without_graph, width=13, height=9, dpi = 320)

Node types for Networkx (python) attributes

library(tidyverse)
library(magrittr)

Results <- read_csv("Results.csv")

data_Networkx <- Results%>% 
         mutate(`Node_type` = ifelse(str_detect(Formula, regex('\\[[a-z]A\\]',              ignore_case = T)), 'Astrocyte', NA)) %>% 
         mutate(`Node_type` = ifelse(str_detect(Formula, regex('\\[[a-z]N\\]',              ignore_case = T)), 'Neuron', `Node_type`))    %>% 
         mutate(`Node_type` = ifelse( str_detect(Formula, regex('\\[[a-z]A\\]|\\[[a-z]N\\]',ignore_case = T), negate = T), 'Exchange', `Node_type`)) %>%
         mutate(`Node_type` = ifelse(str_detect(Formula, regex('\\[[a-z]A\\]',ignore_case = T)) & 
                                       str_detect(Name, regex('DM_|Demand|sink',ignore_case = T)), 'Sink/Demand Astro', `Node_type`))%>%
         mutate(`Node_type` = ifelse(str_detect(Formula, regex('\\[[a-z]N\\]',ignore_case = T)) & 
                                       str_detect(Name, regex('DM_|Demand|sink',ignore_case = T)), 'Sink/Demand Neuron', `Node_type`))


data_Networkx %<>% select(c(ID, Node_type))

write_csv(data_Networkx, 'data_Networkx.csv')